Metadata driven dataset management

ABSTRACT

A method for configuring the operation of the software of a data as a service (DAAS) system during run time is described. The configuring includes at least one of configuring ingestion of a vendor dataset to produce an ingested dataset and which analysis operations to perform on the vendor dataset to produce an analyzed dataset, and the configuring also includes at least one of how to search the vendor dataset based on a search query from a customer to allow the customer to locate a new record from the vendor dataset and how to match records in the vendor dataset with a match query from the customer to provide an updated record to the customer.

TECHNICAL FIELD

One or more implementations relate to the field of data ingestion,analysis, and querying; and more specifically, to utilizing metadata atrun time of a data as a service (“DaaS” or “DAAS”) system forconfiguration of ingesting, analyzing, indexing, and querying vendordatasets to provide records to customers.

BACKGROUND

In the context of a DAAS system, a vendor may provide a vendor datasetfor ingestion into the DAAS system using vendor-specific configurationinformation. In this example, ingestion is performed on the vendordataset by an ingestion service that is configured according to thevendor-specific configuration information. For ingestion, thevendor-specific configuration information may include informationdescribing the structure of the vendor dataset that may be used by theDAAS to validate the vendor dataset and support retrieving records fromthe vendor dataset in support of match and search services (e.g.,locating records in the vendor dataset based on search and match queriesto import new records into a customer system or import updated recordsinto a customer system).

The vendor dataset may also be optionally analyzed by the DAAS system toproduce an analyzed dataset using vendor-specific configurationinformation that configures an analysis service. This analysis mayinclude analysis operations performed on the vendor dataset afteringestion and indicated in the vendor-specific configuration information(in other words, the vendor-specific configuration information may alsoinclude indications of which analysis operations to perform). Theanalyzed dataset produced by this analysis is used by a search servicefor allowing customers to perform search queries to import records fromthe vendor dataset.

The vendor dataset may also be optionally indexed by the DAAS system toproduce an indexed dataset using vendor-specific configurationinformation that configures an indexing service. The indexing may beperformed based on match keys specified in the vendor-specificconfiguration information. The indexed dataset may support performanceof match queries to match records of records already imported intocustomer systems with potentially updated versions in the vendor datasetby a match service. The match service may be configured to support matchqueries using the vendor-specific configuration information, whichincludes indications of the match keys to generate for a correspondingmatch query, match rules to perform the match query, and indications ofweights to apply to perform ranking of results of the match query.

Traditionally, vendor-specific configuration information for datasetingestion, analysis, indexing, and querying must be setup prior torunning the DAAS system. In this fashion, the vendor-specificconfiguration information, including information describing thestructure of the vendor dataset, indications of which analysisoperations to perform, indications of fields to generate the set ofmatch keys to generate, indications of match rules to apply, andindications of weights to apply, is determined and used to configure theDAAS system prior to compilation and running of the DAAS system.However, this technique is inefficient as it requires recompilation andredistribution of the DAAS system software, including the ingestionservice, the analysis service, the indexing service, and the matchservice for any updates to the vendor-specific configurationinformation.

BRIEF DESCRIPTION OF THE DRAWINGS

The following figures use like reference numbers to refer to likeelements. Although the following figures depict various exemplaryimplementations, alternative implementations are within the spirit andscope of the appended claims. In the drawings:

FIG. 1 shows a block diagram of a DAAS architecture that provideson-demand data services according to one example implementation.

FIG. 2 shows a more detailed block diagram of a DAAS system that hasmetadata driven ingestion, analysis, indexing, and querying according toone example implementation.

FIG. 3 shows a set of analysis operations according to one exampleimplementation.

FIG. 4 shows a state machine that represents a data analysis dictionaryaccording to one example implementation.

FIG. 5 shows a set of analysis types defined according to the statemachine in one example implementation.

FIG. 6 shows an example of a data analysis dictionary comprising ananalysis type according to one example implementation.

FIG. 7 shows ingestion/search metadata, including ingestion metadata andsearch metadata, according to one example implementation.

FIG. 8 shows two match keys for a record in an indexed dataset accordingto one example implementation.

FIG. 9 shows an example of match key metadata according to one exampleimplementation.

FIG. 10 shows a detailed block diagram of a match service according toone example implementation.

FIG. 11 shows an example of a logical operation defined in metadataaccording to one example implementation.

FIG. 12 shows a logical operation defined in metadata according toanother example implementation.

FIG. 13 shows a match rule defined in the match rule metadata accordingto one example implementation.

FIG. 14 shows ranking metadata according to one example implementation.

FIG. 15 shows a method according to one example implementation forenabling the configuration of at least one of how to ingest a vendordataset to produce an ingested dataset, which analysis operations toperform on a vendor dataset and which match keys, match rules, and/orweights to use on an ingested dataset responsive to ingestion/searchmetadata and match metadata, including match key metadata, match rulemetadata, and/or ranking metadata during runtime of the DAAS system asopposed to recompilation and redistribution of the DAAS system.

FIG. 16 illustrates an electronic device according to one exampleimplementation.

FIG. 17 shows a block diagram of an environment where an on-demand,metadata driven DAAS system may be implemented.

DETAILED DESCRIPTION

FIG. 1 is a block diagram of a DAAS architecture 100 that provideson-demand data services according to one example implementation. FIG. 1divides the DAAS architecture 100 into three portions: a vendor side102, including a set of vendor systems 104 ₁-104 _(N) (where N isgreater than or equal to one); a customer side 106, including a set ofcustomer systems 108 ₁-108 _(M) (where M is greater than or equal toone); and a DAAS system 110. In this configuration, the vendor systems104 may provide vendor datasets that are ingested, analyzed, and/orindexed by the DAAS system 110. The DAAS system 110 may thereafter usethese ingested, analyzed, and/or indexed datasets available to thecustomer systems 108 for querying and importing desired records intodatabases of the customer systems 108. This querying and importation maybe performed based on (1) a search query 142 that seeks to provide acustomer system 108 with new records from a vendor dataset forimportation and/or (2) a match query 144 that seeks to provide acustomer system 108 with updates to records already imported into thecustomer system 108. For example, a vendor, using one of the vendorsystems 104, may provide to the DAAS system 110 a vendor dataset relatedto business leads. The vendor dataset, which may be transformed via aningestion service 112 to produce an ingested dataset, via an analysisservice 116 to produce an analyzed dataset, and/or via an index service116 to produce an indexed dataset, is stored and persisted in one ormore databases. A customer, using one of the customer systems 108 andhaving contracted with an administrator of the DAAS system 110, mayrequest the DAAS system 110 to query one or more vendor datasets forbusiness leads that meet the customer's purposes (e.g., leads related tothe sale of office equipment). The DAAS system 110 comprises softwarerunning on hardware as described in more detail below.

To improve the production of a query result by the DAAS system 110, thevendor datasets provided by the vendor systems 104 may need to beingested using a particular set of field mappings for verificationpurposes; analyzed using a particular ordered set of one or moreanalysis operations for use when applying a search query 142; andprocessed using a particular set of match keys, match rules, and weightswhen applying a match query 144. This improvement may include one ormore of improving the accuracy, efficiency, and speed of producing aquery result for both search queries 142 and match queries 144. In someimplementations, fine-tuning the ingestion, analysis, indexing, andquerying (for both search queries 142 and match queries 144) to maintainor realize this improvement may be an iterative or continually changingprocedure. As used herein, “accurate query result” refers to theprovision of the data, from data managed by the DAAS system 110, thatmost closely aligns with a corresponding search query 142 and/or matchquery 144. The use of the ingestion/search metadata 122 and the matchmetadata 146 allows the ingestion service 112, the analysis service 116,the index service 118, the search service 130, and/or the match service140 to be configured and/or reconfigured during run time of the DAASsystem 110. In other words, the ingestion/search metadata 122 and/or thematch metadata 146, including one or more of the match key metadata 124,the match rule metadata 126, and the ranking metadata 128, may bealtered (as illustrated by the dashed lines 132, 134, 136, and 138,respectively) during run time of the DAAS system 110, thereby alteringthe ways in which vendor datasets are ingested, analyzed, indexed,and/or queried (for both search queries 142 and match queries 144) bythe DAAS system 110 during run time of the DAAS system 110. Accordingly,through the use of the ingestion/search metadata 122 and/or the matchmetadata 146 that is used during run time, configuration of ingestion,analysis, indexing, and/or querying (for both search queries 142 andmatch queries 144) may be adjusted without recompilation andredistribution of the DAAS system 110 software, including the ingestionservice 112, the analysis service 116, the indexing service 118, thesearch service 130, and the match service 140. In this fashion, the DAASsystem 110, including the ingestion service 112, the analysis service116, the indexing service 118, the search service 130, and the matchservice 140, may, and at times will, remain unchanged (e.g., does notrequire recompilation and redistribution) but ingestion of vendordatasets, analysis of vendor datasets, indexing of vendor datasets,and/or querying of vendor datasets will be altered via modifications tothe ingestion/search metadata 122 and/or the match metadata 146. Thisimproves the efficiency (e.g., the speed, cost, etc.) with which theconfiguration of the ingestion service 112, the analysis service 116,the index service 119, the search service 130, and/or the match service140 may be improved, which improves the production of a query result(for both search queries 142 and match queries 144) by the DAAS system110 (e.g., adjustments may be made quickly and less expensivelyresponsive to discovering better configurations that require lessstorage, require less processing, produce more accurate query results,and/or improve the speed with which a query result can be generated). Inparticular, through the use of the ingestion/search metadata 122 and/orthe match metadata 146 the DAAS system 110 may easily ingest, analyze,index, and query data from various vendors with corresponding differentdata configurations (e.g., use of different field names) that may beconfigured to map to structures understood within the DAAS system 110(e.g., mapping of field names in the vendor datasets to fields of theDAAS system 110).

In some implementations, the DAAS system 110 may be a multi-tenantsystem. As a multi-tenant DAAS system, it may include a single instanceof each of the ingestion service 112, the analysis service 116, and theindex service 118 that may be accessed by multiple vendor systems 104(also known as tenants) and each vendor system 104 is provided with adedicated share of a software instance of each of the ingestion service112, the analysis service 116, and the index service 118. Similarly, theDAAS system 110 may include a single instance of each of the searchservice 130 and/or the match service 140 that may be accessed bymultiple vendor systems 104 and each vendor system 104 is provided witha dedicated share of a software instance of each of the search service130 and/or the match service 140. In this multi-tenant DAAS system 110,the ingestion/search metadata 122 and the match metadata 146, includingthe match key metadata 124, the match rule metadata 126, and/or theranking metadata 128, may be used for configuring one or more of theshares of the software instances of the ingestion service 112, theanalysis service 116, the index service 118, the search service 130,and/or the match service 140 (e.g., in a multi-tenant DAAS system,configuring a share of the ingestion service 112, a share of theanalysis service 116, a share of the index service 118, a share of thesearch service 130, and/or a share of the match service 140 specificallyfor each vendor based on that vendor's specific configurationinformation).

In other implementations, the DAAS system 110 may be a single-tenantsystem. As a single-tenant system, the ingestion/search metadata 122and/or the match metadata 146 may be used for configuring the ingestionservice 112, the analysis service 116, the index service 118, the searchservice 130, and/or the match service 140 in a similar fashion asdescribed above in relation to the DAAS system 110 operating as amulti-tenant system. Accordingly, the implementation of the DAAS system110 as a multi-tenant or single-tenant system does not alter thefunctionality of the DAAS system 110 and associated components describedherein.

FIG. 2 shows a more detailed block diagram of a DAAS system 110 that hasmetadata driven ingestion, analysis, indexing, and querying (for bothsearch queries 142 and match queries 144) according to one exampleimplementation. As shown in FIG. 2, the DAAS system 110 receives, viathe ingestion interface 114, a vendor dataset 202 for ingestion by theingestion service 112 to produce an ingested dataset 204. In oneimplementation a vendor dataset 202 comprises data stored in one or morefields of one or more data structures, and each of these fields may ormay not be defined within the DAAS system 110. The ingestion metadata206 (e.g., the entirety or a portion of the ingestion/search metadata122) includes mappings of fields used within the vendor dataset 202 tofields defined within the DAAS system 110. In one implementation, themappings included within the ingestion metadata 206 may be used by theingestion service 112 to verify that the structure of the vendor dataset202 (e.g., the set of fields in the vendor dataset 202) complies with astructure agreed on between a vendor of the vendor system 104 providingthe vendor dataset 202 to the DAAS system 110 and an administrator ofthe DAAS system 110. The ingested dataset 204 may be a combination ofthe vendor dataset 202 and the ingestion metadata 206. The ingesteddataset, including the vendor dataset 202 and the ingestion metadata206, may be later used by various other components of the DAAS system110 (e.g., the analysis service 116, the index service 118, the searchservice 130, and/or the match service 140) for accessing data from thevendor dataset 202 (e.g., for cross-referencing records in the vendordataset 202 with records in a dataset generated based on the vendordataset 202). In some implementations, the ingestion interface 114 maypermit a vendor system 104 to deliver a vendor dataset 202 to the DAASsystem 110 for ingestion over a network. The network may comply with oneor more network protocols, including an Institute of Electrical andElectronics Engineers (IEEE) protocol, a 3rd Generation PartnershipProject (3GPP) protocol, or similar wired and/or wireless protocols, andmay include one or more intermediary devices for routing data (e.g., avendor dataset 202) from a vendor system 104 to the DAAS system 110. Insome implementations, the ingestion interface 114 may utilize a FileTransfer Protocol (FTP), a Network File System (NFS), or a similarprotocol/system.

In one implementation, the vendor dataset 202, provided by the vendorsystem 104 and associated with a vendor, may include any set of data andmay be represented in any format. In one implementation, the vendordataset 202 may be vendor data that is intended to be made accessible toone or more of the customers and/or customer systems 108. For example,the vendor dataset 202 may include company and contact information for aset of business leads and may be represented in a flat databasestructure. In this flat database structure, the vendor dataset 202 maybe represented in a single table (or database object) or as a singledata record, which is separated by delimiters, such as tabs or commas(e.g., Comma-Separated Values (CSV) or JavaScript Object Notation (JSON)file). In other implementations, the vendor dataset 202 may be providedas a set of relational database tables (or database objects) or inanother type of database structure or data format.

In some implementations, the ingestion metadata 206 may be a portion ofthe ingestion/search metadata 122. For example, a portion of theingestion/search metadata 122 (e.g., the ingestion metadata 206) may beused for configuring the ingestion service 112 and later used by theanalysis service 116, the index service 118, the search service 130, andthe match service 140 for accessing data within the vendor dataset 202while another portion of the ingestion/search metadata 122 (e.g., thesearch metadata 214) may be used for configuring the analysis service116. In other implementations, the ingestion/search metadata 122 may beentirely composed of the ingestion metadata 206 or entirely composed ofthe search metadata 214.

In some implementations, the mappings, defined by the ingestion metadata206, between fields of the vendor dataset 202 and fields of the DAASsystem 110 may be determined in several different ways. For example, theingestion metadata 206, defining the mappings, may be generated (1)manually based on inputs from a vendor associated with the vendor system104 and/or a representative of the DAAS system 110 (e.g., inputsreceived via an Application Programming Interface (API) and/or aGraphical User Interface (GUI)); (2) automatically (e.g., determiningingestion metadata 206 by mapping fields of a vendor dataset 202 tofields defined within the DAAS system 110 that share similar names,including through the use of machine learning algorithms); and/or (3) acombination of manual input and automation. For example, in response tonot receiving or locating ingestion metadata 206 associated with avendor dataset 202, the DAAS system 110 may be triggered toautomatically generate ingestion metadata 206 for the vendor dataset202. In this implementation, the DAAS system 110 may utilize machinelearning to automatically generate ingestion metadata 206 for the vendordataset 202 based on field names or field values of the vendor dataset202. The automatically generated ingestion metadata 206 may be latermodified by a vendor associated with the vendor system 104 and/or arepresentative of the DAAS system 110.

As noted above, the ingestion metadata 206 may be specific for orassociated with each vendor system 104 and/or each vendor and may beused at run time of the DAAS system 110 for configuring the DAAS system110 to generate the ingested dataset 204 and later used by the analysisservice 116, the index service 118, the search service 130, and thematch service 140 for accessing data within the vendor dataset 202. Byusing the ingestion metadata 206 at run time, the process of ingesting avendor dataset 202 and accessing the vendor dataset 202 may be adjustedwithout a need for recompilation and redistribution of the DAAS system110 software, including the ingestion service 112, the analysis service116, the index service 118, the search service 130, and the matchservice 140.

Following generation of the ingested dataset 204, which may be comprisedof the vendor dataset 202 and the ingestion metadata 206, the ingesteddataset 204 may be stored (e.g., in direct pipeline storage) pendingfurther processing by the DAAS system 110.

The ingested dataset 204 may be received and analyzed by an analysisservice 116. More specifically, the vendor dataset 202 within theingested dataset 202 may be analyzed (based on mappings in the ingestionmetadata 206) using an ordered set of one or more analysis operationsthat are referenced in search metadata 214 (e.g., the entirety or aportion of the ingestion/search metadata 122). These analysis operationsplace the vendor dataset 202, that is part of the ingested dataset 204,in a form that will produce improved search query results 216A for thecustomer systems 108 than the vendor dataset 202 on its own.

The search metadata 214 may indicate the ordered set of one or moreanalysis operations to be performed differently in differentimplementations (e.g., implementations may support indicating theanalysis operations individually, using one or more “analysis types,” orindicating one or more of the set individually and the rest using one ormore “analysis types”). In an implementation that supports analysistypes, each “analysis type” identifies an ordered set of one or moreanalysis operations. While different implementations may store thisinformation differently, in one implementation that supports analysistypes, the different analysis types are defined in a data analysisdictionary 212. For example, an analysis type, in the data analysisdictionary 212, may be defined by an ordered set of analysis operationsthat are associated with an identifier. For instance, a first orderedset of analysis operations define a first analysis type associated witha first identifier and a second ordered set of analysis operationsdefine a second analysis type associated with a second identifier. Inthis example implementation, the search metadata 214 may reference thefirst identifier to perform the first ordered set of analysis operationson a field of the vendor dataset 202 and/or may reference the secondidentifier to perform the second ordered set of analysis operations onanother field of the vendor dataset 202. Accordingly, the analysis typesact as shorthand to reference an ordered set of analysis operations.

As noted above, the analysis types may be defined in a data analysisdictionary 212. The data analysis dictionary 212 may be determined basedon inputs from a vendor associated with the vendor system 104 and/or arepresentative of the DAAS system 110 (e.g., inputs received via an APIand/or a GUI during run time of the DAAS system 110). The data analysisdictionary 212 may be specific for or associated with each vendor system104 and/or each vendor. Thus, in such an implementation, one may createan analysis type in the data analysis dictionary 212, and then one mayrefer to that analysis type in search metadata 214, and this referencein the search metadata 214 is converted into the ordered set of analysisoperations by the analysis service 116.

The analysis operations used by the analysis service 116 may involvevarious transformations and actions to be performed on the vendordataset 202 to generate an analyzed dataset 210. In oneimplementation: 1) the vendor dataset 202 comprises data stored in oneor more fields of one or more data structures; 2) the analysis service116 uses the search metadata 214 (e.g., the entirety or a portion of theingestion/search metadata 122), which identifies an ordered set ofanalysis operations to be performed on the vendor dataset 202, toproduce the analyzed dataset 210; 3) the analyzed dataset 210 comprisesdata stored in one or more fields of one or more data structures; and 4)the fields of the analyzed dataset 210 may include additional fields (asdescribed below) not found in the vendor dataset 202 and/or not includesome fields found in the vendor dataset 202.

After the analyzed dataset 210 has been generated, it is stored in theinfo retrieval system 218 and the search service 130 may providemechanisms to provide search query results 216A to customer systems 108and customers responsive to receiving a communication from customersystems 108 (e.g., a search query 142; responsive to a user interactingwith a GUI on a device in communication with an application provided inthe cloud, the application generates the search query 142). As shown inFIG. 2, the search query result 216A may be provided to consumer systems108 via the serving interface 120. In some implementations, the servinginterface 120 may provide the search query result 216A over a network.The network may comply with one or more network protocols, including anIEEE protocol, a 3GPP protocol, or similar wired and/or wirelessprotocols, and may include one or more intermediary devices for routingdata from the DAAS system 110 to a customer system 108. In someimplementations, the serving interface 120 may utilize a FTP, a NFS, ora similar protocol/system.

FIG. 3 shows a set of analysis operations 300 according to one exampleimplementation. As shown in FIG. 3, the analysis operations 300 mayinclude one or more normalization operations 302 to be performed on avendor dataset 202. Normalization may remove data redundancy in a vendordataset 202 and thereby simplify the design of the vendor dataset 202.In some implementations, the normalization operations may include one ormore of first through sixth normalization operations, a Boyce-Coddnormalization operation, or a similar normalization operation.

In one implementation, the analysis operations 300 may additionally oralternatively include one or more text analysis operations 304. The textanalysis operations 304 may include one or more tokenization operations304A to be performed on a vendor dataset 202. In some implementations,tokenization may include splitting a stream of data (e.g., a field valueof the vendor dataset 202) into separate pieces (e.g., tokens). Forexample, a whitespace tokenizer may split a field value of a vendordataset 202 into separate tokens upon encountering whitespace in thefield value. In another example, a tokenizer may split a field valueinto separate tokens upon encountering whitespace or any punctuation inthe field value of the vendor dataset 202.

In one implementation, the text analysis operations 304 may additionallyor alternatively include one or more character filtering operations 304Bto be performed on a vendor dataset 202. Character filtering operations304B may include removing specific characters from a vendor dataset 202.For example, the characters #, @, and & may be removed from a fieldvalue of an ingested dataset 204 using one or more character filteringoperations 304B.

In one implementation, the text analysis operations 304 may additionallyor alternatively include one or more token filtering operations 304C.Token filtering operations 304C perform adjustments or otherwisetransform tokens according to specified criteria. For example, the tokenfiltering operations 304C may perform one or more of converting all textin a token to lowercase, converting all text in a token to uppercase,adjusting a token to support stemming, injecting elements into a tokento cover synonyms, and removing elements from tokens, including stopwords. In some implementations, multiple token filtering operations 304Cmay be chained together to form a single token filtering operation 304C.

In one implementation, the analysis operations 300 may additionally oralternatively include one or more data field generation operations 306.Data field generation operations 306 may include combining data from twoor more fields of a vendor dataset 202 to generate data for anadditional field. This additional field is also defined within the inforetrieval system 218. In one example, a “Company Name” field may becombined with a “Company Address” field to generate a “CompanyIdentifier” field. In this example implementation, the “CompanyIdentifier” field may be expressed as {Company Name, Company Address}.Although described as a direct combination of fields, in otherimplementations, data for an additional field may be generated based ononly a portion of an existing field value or based on a function of anexisting field value. In some implementations, data for an additionalfield may be generated using a data field generation operation 306 basedon previously generated fields using a data field generation operation306. For example, the “Company Identifier” field may be combined with a“CEO” field to generate a “Corporate Governance” field.

In one implementation, the analysis operations 300 may additionally oralternatively include one or more indexing operations 308. Indexingoperations 308 may include collecting, parsing, and storing data tofacilitate efficient record retrieval. Any type of indexing may beperformed using the indexing operations 308, including bitmap indexing,dense indexing, sparse indexing, reverse indexing, or any combinationthereof.

In one implementation, the analysis operations 300 may additionally oralternatively include a storing operation 310. The storing operation 310may store a field from the vendor dataset 202 in the analyzed dataset210. Accordingly, the field stored in the analyzed dataset 210 is rawdata from the vendor dataset 202.

In some implementations, the analysis operations 300 may be vendorsystem 104 specific or vendor specific and may be adjusted while theDAAS system 110, including the analysis service 116, is running. Forexample, a first text analysis operation 304 may remove all white spacefrom a field. A vendor or administrator of the DAAS system 110 maydetermine that this first text analysis operation 304 is no longerdesirable and may decide to replace this first text analysis operation304 with a second text analysis operation 304 that removes whitespaceonly from the beginning and ends of the field. The first text analysisoperation 304 may be replaced with the second text analysis operation304 without recompilation and redistribution of the DAAS systemsoftware.

As described above, one or more analysis operations 300 may be combinedtogether to form analysis types. These analysis types may operate on afield in a vendor dataset 202. For example, an analysis type may becomprised of a normalization operation 302 and a indexing operation 308.In this example, normalization may be performed on a field of a vendordataset 202 (e.g., a “Company Name” field) using the normalizationoperation 302 and the resulting normalized field may thereafter beindexed using the indexing operation 308. This combination of analysisoperations 300 may define an analysis type. Although described ascombining two analysis operations 300, in other implementations anynumber of analysis operations 300 may be combined to form an analysistype.

In one implementation, analysis types may be defined by a data analysisdictionary 212. In this implementation, each analysis type is comprisedof a different set of ordered groups of analysis operations 300, whichare each performed on a field, as defined by the data analysisdictionary 212. For example, FIG. 4 shows a state machine 400 thatrepresents a data analysis dictionary 212 according to one exampleimplementation. The state machine 400 of FIG. 4 defines a set ofavailable analysis types according to one example implementation. Inthis example, the state machine 400 includes a set of states,corresponding to analysis operations 300 and a start position (e.g.,start 402), and the states are interconnected by a set of directedvertices. Each pass through the state machine 400 (e.g., a pathwaythrough the states via the vertices) defines a separate analysis typebased on the ordered group of states or analysis operations 300 in eachpathway. In some implementations, a single analysis type may be definedby multiple passes through the state machine 400 using the same field ofthe vendor dataset 202. Although the data analysis dictionary 212 isrepresented by a state machine 400 in the example implementation of FIG.4, in other implementations, different structures may be used forestablishing rules and relationships between analysis operations 300 todefine analysis types.

As shown, the state machine 400 may begin at the start 402 state withthe selection of a field within a given vendor dataset 202. Uponselection of a field, a first analysis operation 300 may be performed.In this example state machine 400, the first analysis operation 300would either be a normalization operation 302, a text analysis operation304, a data field generation operation 306, an indexing operation 308,or a storing operation 310. Upon selection of a first analysis operation300, the state machine 400 indicates whether another analysis operation300 is necessary or whether the analysis is complete. This relationshipbetween analysis operations 300 is shown by vertices between states inthe state machine 400. For example, upon selection of a normalizationoperation 302, the state machine 400 indicates that either a textanalysis operation 304, an indexing operation 308, or a storingoperation 310 must be performed. This is indicated by vertices pointingfrom the normalization operation 302 state to each of the text analysisoperation 304 state, the indexing operation 308 state, and the storingoperation 310 state.

FIG. 5 shows a set of analysis types 500 defined according to a singlepass through the state machine 400 of FIG. 4 in one exampleimplementation. Although not shown in FIG. 5, each of the analysis types500A-500J are performed after selection of a field (e.g., at start 402)of the vendor dataset 202 upon which the corresponding analysis type 500will operate. In some implementations, selection of a field may beconsidered an analysis operation 300.

Each of the analysis types 500 may be associated with an identifier. Forexample, an analysis type 500 may be associated with the identifier“knownNormalizedStemFacetText.” This analysis type 500 may specify anordered set of one or more analysis operations 300. The search metadata214 may use this identifier (e.g., knownNormalizedStemFacetText) toreference the associated analysis type 500 instead of referencing theanalysis operations 300 individually and in the corresponding order.Accordingly, analysis types 500 provide a more efficient way ofreferencing analysis operations 300 by allowing a reference to ananalysis type 500 for use of an ordered set of analysis operations 300.

Although the data analysis dictionary 212 is represented as a graphicalstate machine 400, in some implementations the data analysis dictionary212 may be represented in another form that is more easily pareseable bythe DAAS system 110. For example, FIG. 6 shows an example of a dataanalysis dictionary 212 comprising the analysis types 500A-500Eaccording to one example implementation. As shown, the data analysisdictionary 212 of FIG. 6 is represented using XML, however, in otherimplementations, other data formats or representations may be utilized.In one implementation, a set of APIs, which allow for a data analysisdictionary 212 to be defined by a vendor through a vendor system 104and/or by a representative of the DAAS system 110, may be provided fordefining the analysis types 500. For example, a representative of theDAAS system 110 may include references to an ordered set of analysisoperations 300 in the analysis dictionary 212 to define an analysis type500. Accordingly, the data analysis dictionary 212 defines analysistypes 500, which are each separate combinations of analysis operations300 in a selected order, and the search metadata 214 references theseanalysis types 500 such that the analysis service 116 performs theanalysis types 500 (e.g., the ordered set of analysis operations 300defined for each respective analysis type 500) on fields as indicated inthe search metadata 214. The data analysis dictionary 212 may bereceived by the DAAS system 110 at run time of the DAAS system 110,including run time of the analysis service 116, such that the DAASsystem 110 may be configured to analyze the ingested dataset 204, and inparticular the vendor dataset 202 within the ingested dataset 204, usingthe search metadata 214 based on references in the search metadata 214to analysis types 500 defined in the data analysis dictionary 212.

In one implementation, data analysis dictionaries 212 may be vendorsystem 104 and/or vendor specific. Accordingly, each vendor system 104may have a separate data analysis dictionary 212 indicating separateanalysis types 500 that may be used on respective vendor datasets 202.In another implementation, a data analysis dictionary 212 may be usedacross several or all vendor systems 104 in the DAAS architecture 100.Accordingly, in this implementation, analysis types 500 (also referredto as analyzer types) may be shared across vendor systems 104.

As noted above, in some implementations, the search metadata 214 may beused for referencing analysis types 500, and consequently the analysisoperations 300 that comprise each analysis type 500, that are used toprocess particular fields of a vendor dataset 202 to generate theanalyzed dataset 210. Similar to the ingestion metadata 206, the searchmetadata 214 may be used at run time of the DAAS system 110 forconfiguring the DAAS system 110, including the analysis service 116. Byusing the search metadata 214 at run time, the process of analyzing avendor dataset 202 may be adjusted without a need for recompilation andredistribution of the DAAS system 110 software.

FIG. 7 shows ingestion/search metadata 122, including the ingestionmetadata 206 and the search metadata 214, according to one exampleimplementation. As shown in FIG. 7, the ingestion/search metadata 122may be represented using XML. However, in other implementations,different languages, data structures, or formats may be used forrepresenting the ingestion/search metadata 122. In some implementations,the ingestion/search metadata 122 may be associated with and determinedby a vendor and/or a representative of the DAAS system 110 via an APIand/or GUI. Accordingly, in these implementations, the ingestion/searchmetadata 122 for a particular vendor system 104 may be determined by theinputs from a vendor and/or a representative of the DAAS system 110.

Although shown in FIG. 7 as the ingestion metadata 206 and the searchmetadata 214 being within a unified structure (e.g., theingestion/search metadata 122), as described above the ingestionmetadata 206 and the search metadata 214 may be within separate datastructures.

As shown in FIG. 7, a field named “Company Name”, which maps to the“COMPANY_NAME” field from the vendor dataset 202, has the “Analyzer4”analysis type 500 applied to it. Although shown in FIG. 7 as applying ananalysis type 500 to a single field, similar ingestion/search metadata122 may be used in the same ingestion/search metadata 122 to applyanalysis types 500 to other fields.

In some implementations, the analyzed dataset 210 generated by theanalysis service 116 may be stored within the info retrieval system 218.In some implementations, the search service 130 may perform searchqueries 142 using the analyzed dataset 210 accessed from the inforetrieval system 218 to produce a search query result 216A. In theseimplementations, the analyzed dataset 210 may be used for performing asearch query 142. The search query 142 describes particular recordswithin the vendor dataset 202 that a customer would like to import intothe customer system 108. For example, a customer may like to importrecords from the vendor dataset 202 describing business contacts withina customer relation management system of the customer system 108. Thesearch service 130 may locate records in the analyzed dataset 210 thatmeet criteria within the search query 142 and return the correspondingrecords from the vendor dataset 202 to the customer system 108 as asearch query result 216A. In this implementation, records within theanalyzed dataset 210 that meet the criteria of the search query 142 arecross-referenced against the vendor dataset 202 to locate correspondingrecords in the vendor dataset 202 that are returned to the customersystem 108 via the serving interface as the search query results 216A.

In some implementations, the index service 118 may index the ingesteddataset 204, and in particular the vendor dataset 202 within theingested dataset 204, based on match metadata 146 to support performinga match query 144 by the match service 140. In these implementations,the index service 118 may receive the match key metadata 124 to generatea set of match keys upon which the vendor dataset 202 will be indexed togenerate the indexed dataset 220.

Match keys are combinations of two or more fields or individual fieldsused by the vendor dataset 202. For instance, if the vendor dataset 202includes a Company Name field, a Country field, a Phone Number field,and a Domain field, then one match key may be a combination of values ofthe Domain field and the Country field. For example, a match key may bethe concatenation of a Domain field value with a Country field value foreach record in the vendor dataset 202. FIG. 8 shows two match keys 801Aand 801B for a record 803 in the vendor dataset 202 according to oneexample implementation. The match key 801A is the combination of theDomain field and the Country field while the match key 801B is only theDomain field for the record 803.

As noted above, match keys are generated by the index service 118 foreach record in the vendor dataset 202. Based on one or more of thesegenerated match keys, the vendor dataset 202 may be indexed by the indexservice 118 to generate the indexed dataset 220.

In one implementation, the set of match keys used to index the vendordataset 202 are defined in match key metadata 124. FIG. 9 shows anexample of match key metadata 124 according to one exampleimplementation. As shown, the match key metadata 124 may be representedusing XML. FIG. 9 shows examples of match key definition 901A and matchkey definition 901B. The match key definition 901A is named“DOMAIN_COUNTRY” and corresponds to the match key 801A and the match keydefinition 901B is named “DOMAIN” and corresponds to the match key 801B.Throughout this description, match key may be used synonymously withmatch index. The match key metadata 124 may be used to configure theindexing performed by the index service 118 during run time of the DAASsystem 110, including at run time of the index service 118. Since thematch key metadata 124 may be used at run time to configure the DAASsystem 110, recompilation and/or redistribution of the DAAS systemsoftware and/or the index service 118 is not necessary.

In some implementations, one or more operations may be performed on afield prior to generating a match key. For example, as shown in FIG. 9for the match key definition 901A, an IndexDomainNormalizer may beperformed on the Domain field and the PlainNormalizer may be performedon the Country field. For the match key definition 901B, theIndexDomainNormalizer may be performed on the Domain field. Normalizersmay help in identifying the main component of the field. For instance,the IndexDomainNormalizer, removes the beginning protocol “http://www.”and returns the main or remaining portions of the Uniform ResourceLocator (URL) value. In one implementation, a phonetic index normalizermay be used to assist in retrieving similar sounding company names(e.g., Printronics and Printronix normalizers). Although described asusing normalizers, in other implementations, other operations may beperformed on a field prior to generation of match keys.

Match key metadata 124 may be based on inputs from one or moreadministrators of the DAAS system 110 and/or from a vendor of a vendorsystem 104. In some implementations, match key metadata 124 may bevendor system 104 or vendor specific.

The indexed dataset 220 generated by the index service 118 may be storedin the info retrieval system 218 such that a match service 140 mayperform a match query 144 using the indexed dataset 220 retrieved fromthe info retrieval system 218 to update records previously imported by acustomer system 108. For example, a customer system 108 may include acustomer relationship management system. A customer using the customersystem 108 may be reviewing a set of records previously imported fromthe DAAS system 110 (e.g., using the search service 130). The customerrelationship management system may include an interface that allows thecustomer to trigger the update of records in the customer relationshipmanagement system that were previously imported from the DAAS system110. Triggering this update causes the customer relationship managementsystem to generate and transmit a match query 144 to the DAAS system110. In response to the match query 144, the match service 140 mayproduce a match query result 216B that includes records to be importedby the customer system 108. The customer system 108 may use the recordsin the match query result 216B to update previously imported recordsstores within the customer system 108.

FIG. 10 shows a detailed block diagram of the match service 140according to one example implementation. As shown in FIG. 10, the matchservice 140 may include four operations: a candidate finder operation1001, a matching operation 1003, a ranking operation 1005, and an enrichoperation 1011. For each match query 144 performed by the match service140, the match service 140 may produce a match query result 216B that isprovided to a corresponding customer system 108 and customer thatrequested the match query 144 to be performed. In some implementations,the match service 140 may utilize one or more of match key metadata 124for configuration of the candidate finder operation 1001, match rulemetadata 126 for configuration of the matching operation 1003, andranking metadata 128 for configuration of the ranking operation 1005.The match key metadata 124, the match rule metadata 126, and/or theranking metadata 128 may be used to configure the match queryingperformed by the DAAS system 110 during run time of the DAAS system 110,including at run time of the match service 140. Since the match keymetadata 124, the match rule metadata 126, and/or the ranking metadata128 may be used at run time to configure the DAAS system 110,recompilation and/or redistribution of the DAAS system software and/orthe match service 140 is not necessary.

As noted above, the match service 140 may include a candidate finderoperation 1001. The candidate finder operation 1001 may operate on amatch query 144 and an indexed dataset 220 stored in the info retrievalsystem 218. As noted above, the match query 144 may be received from acustomer system 108 and may be associated with a customer. The matchquery 144 may describe criteria of a query to be performed by the matchservice 140. For example, the match query 140 may include a set ofvalues for a set of fields that are defined in the DAAS system 110. Forinstance, if the DAAS system 110 defines a Company Name field, a Countryfield, a Phone Number field, and a Domain field, and these fields ofdifferent data records were populated with values identified for thosefields in the vendor dataset 202 (amongst potentially other fields alsodefined in the DAAS system 110), a match query 144 may include valuesfor the Company Name field, the Country field, the Phone Number field,and the Domain field. For instance, in the above example, a customer isseeking records in the vendor dataset 202 that have values in theCompany Name field, the Country field, the Phone Number field, and theDomain field that match corresponding values in the match query 144.

For a given match query 140, the candidate finder operation 1001determines a set of candidate records 1007 from the indexed dataset 220.The candidate records 1007 are a subset of the records in the indexeddataset 220. In one implementation, the determination of candidaterecords 1007 from the indexed dataset 220 is based on a set of matchkeys that are generated by the candidate finder operation 1001 for thematch query 220 and were previously generated by the index service 118for the vendor dataset 202 (e.g., the index service 118 generated theindexed dataset 220 that includes the match keys for the vendor dataset202). As noted above, match keys are combinations of two or more fieldsor individual fields. The match keys generated by the candidate finderoperation 1001 for the match query 144 are based on the same fields asthose used by the index service 118 to generate the indexed dataset 220.Accordingly, the candidate finder operation 1001 uses the match keymetadata 124 to generate match keys for the match query 144. Recordsfrom the indexed dataset 220 that have match keys that match with matchkeys of the match query 144 are determined to be candidate records 1007.For instance, for the example in FIG. 8, if a match query 144 includes afirst match key with the value “salesforce.com@usa” and a second matchkey with the value “salesforce.com”, the record 803 of FIG. 8 matcheswith the match query 144 and the record 803 is determined to be acandidate record 1007.

Through the use of the candidate finder operation 1001, the candidaterecords 1007 are determined. Although the candidate finder operation1001 narrows down the list of possible records that will match with thematch query 144, the candidate records 1007 determined by the candidatefinder operation 1001 may still contain records that do not match thematch query 144. To correct this issue, the matching operation 1003eliminates non-matching records from the candidate records 1007 toproduce the matched records 1009.

In one implementation, the matching operation 1003 may apply a set ofmatch rules to the candidate records 1007 to determine matched records1009 from the candidate records 107 (i.e., the matched records 1009 arethose records of the candidate records 807 that match the fields of thematch query 144 using a set of methods and/or thresholds). A match rulemay be an equation composed of one or more logical operations and zeroor more operators. Each logical operation compares a field in thecandidate records 1007 with the same field in the match query 144 todetermine a logical value (e.g., a Boolean value).

In one implementation, the set of match rules, including the one or morelogical operations, may be defined using metadata. FIG. 11 shows anexample of a logical operation 1100 defined in metadata (e.g., XML)according to one example implementation. As shown, the logical operation1100 uses an exact match method 1101 to perform a comparison using theStreet Number field 1103. The exact match method 1101 returns a matchscore of “100” when an exact match is determined being a record in thecandidate records 1007 and a match query 144. Otherwise, a match scoreof “0” is returned when any other level of match is determined. Thematch score returned by the exact match method 1101 is compared againstthe threshold 1105 to determine a logical value for the logicaloperation 1100. Since the exact match method 1101 is used for thelogical operation 1100, the threshold 1105 is set to “100”, indicatingthat only a “100” or “100%” match score will cause the logical operation1100 to return a “true” logical value. Otherwise, the logical operation1100 returns a “false” logical value for any other match score.

FIG. 12 shows a logical operation 1200 defined in metadata (e.g., XML)according to another example implementation. As shown, the logicaloperation 1200 uses an edit distance method 1201 to perform a comparisonusing the Street Name field 1203. The edit distance method 1201determines the similarity between two strings based on the number ofdeletions, insertions, and character replacements needed to transformone string into the other. Based on the similarity between the StreetName field 1203 of a record in the candidate records 1007 and the matchquery 144, the edit distance method 1201 returns a match scorereflective of this similarity. For example, a high similarity wouldreturn a high match score (e.g., “98” or “98%) while a low similaritywould return a low match score (e.g., “30” or “30%”). The match scorereturned by the edit distance method 1201 is compared against thethreshold 1205 to determine a logical value for the logical operation1200. In the example of FIG. 12, the threshold is set to “90”,indicating that only a match score of “95” or greater will cause thelogical operation 1200 to return a “true” logical value. Otherwise, thelogical operation 1200 returns a “false” logical value for a match scoreless than “95.”

As noted above, a match rule may be a composed of one or more logicaloperations and zero or more operators. FIG. 13 shows a match rule 1300defined in the match rule metadata 126 according to one exampleimplementation. As shown, the match rule 1300 is composed of the logicaloperation 1100, the logical operation 1200, and the logical operator1301 (i.e., the AND operator). In this example, the logical operator1301 (e.g., the AND operation) is applied using the logical valuereturned from the logical operation 1100 and the logical value returnedform the logical operation 1200. When both the logical operation 1100and the logical operation 1200 return “true” for a record in thecandidate records 1007, the match rule 1300 returns “true” and therecord is included in the matched records 1009 by the matching operation1003. Otherwise, the record is excluded from the matched records 1009 bythe matching operation 1003.

As noted above, match rules may be defined in the match rule metadata126 and the match rule metadata 126 may include one or more match rules.The match rule metadata 126 may be used at run time of the DAAS system110, including the match service 140, for configuration of the DAASsystem 110, including the match service 140. By using the match rulemetadata 126 at run time, match rules may be adjusted without a need forrecompilation and redistribution of the DAAS system software, includingthe match service 140.

Following determining the matched records 1009, the ranking operation1005 may rank the matched records 1009. In one implementation, thematched records 1009 are ranked based on weights applied to match scoresdetermined during the matching operation 1003. For example, the exactmatch method 1101 may return a match score of “100” and the editdistance method 1201 may return a match score of “96” for a particularrecord in the matched records 1009. In this example, the rankingoperation 1005 may apply a first weight to the match score returned bythe exact match method 1101 and a second weight to the match scorereturned by the edit distance method 1201 to produce a set of weightedscores. In one implementation, application of a weight to a match scoremay be performed through multiplication (e.g., weight*match score). Theweighted scores may be summed or averaged together to compute a matchingscore for the record.

The matching score computed for each record in the matched records 1009may be used for ranking the matched records 1009 to generate a set ofranked records 1013. For example, the matched records 1009 may be rankedfrom highest to lowest (e.g., decreasing order) based on the final scoreof each record in the matched records 1009 to generate the ranked set ofrecords 1013. In one implementation, the ranked set of records 1013 is asubset of the matched records 1009. For example, only the records in thematched records 1009 with the highest matching score is in the rankedset of records 1013. In another example, only the top three records inthe matched records 1009 with the three highest matching scores are inthe ranked set of records 1013.

In one implementation, the weights used to generate the weighted scoresand ultimately the matching scores may be defined using the rankingmetadata 128. For example, FIG. 14 shows ranking metadata 128 accordingto one example implementation. In this example, field names are used toreference match scores returned by corresponding methods. For example,the field name “Street_Name” is used to reference the match scorereturned by the exact match method 1101 of the logical operation 1100and the field name “Street_Number” is used to reference the match scorereturned by edit distance method 1201 of the logical operation 1200. Aseparate weight that is indicated in the ranking metadata 128 for eachmatch score. For example, as shown in FIG. 14, a weight of “100” isindicated for the match score returned by the method operating on theStreet_Number field and a weight of “1000” is indicated for the matchscore returned by the method operating on the Street_Name field.

In one implementation, the ranking operation 1005 may also compute anon-matching score for each record in the matched records 1009. In thisimplementation, fields that are not a basis for matching (i.e.,non-matching fields) may be associated with non-matching weights. Forexample, fields that are not defined in the DAAS 110 but are within avendor dataset 202 may include operations and corresponding weights tocompute a non-matching score. For instance, the ranking metadata 128includes an “isHQ” operation that determines whether a correspondingrecord in the matched records 1009 corresponds to a headquarter office.Similarly, the ranking metadata 128 includes an “isBranch” operationthat determines whether a corresponding record in the matched records1009 corresponds to a branch office. When a record in the match records1009 is a headquarter office, the corresponding weight indicated in theranking metadata 128 may be selected for use. Conversely, when a recordin the match records 1009 is a branch office, the corresponding weightindicated in the ranking metadata 128 may be selected for use. Theweights of non-matching fields that are selected for use may be summedtogether to compute a non-matching score for each record in the matchedrecords 1009.

In one implementation, a score ratio may be applied to the matchingscore and/or the non-matching score for each record in the matchedrecords 1009. For example, the ranking metadata 128 may indicate a scoreratio of 0.75. In this example, the final score for each record may becomputed as the sum of the matching score multiplied by 0.75 (e.g., thescore ratio) and the non-matching score multiplied by 0.25 (e.g.,1−score ratio). This final score may be used by the ranking operation1005 for ranking the records in the matched records 1009 to generate theranked set of records 1013.

As described above, weights used to generate the weighted scores may bedefined by the ranking metadata 128. The ranking metadata 128 may beused by the DAAS system 110, including the match service 140, at runtime to configure the DAAS system 110, including the match service 140.By using the ranking metadata 128 at run time, weights may be adjustedwithout a need for recompilation and redistribution of the DAAS system110 software, including the match service 140.

The ranked set of records 1013 may be compared with the ingested dataset204 by the enrich operation 1011, and in particular the vendor dataset202, to determine records in the vendor dataset 202 that match withrecords in the ranked set of records 1013 (e.g., using a primaryidentifier in each of the ranked set of records 1013 and the vendordataset 202). These matching records in the vendor dataset 202 may bereturned to the customer system 108 as the match query result 216B.

Turning now to FIG. 15, a method 1500, according to one exampleimplementation, will be described for enabling the configuration of atleast one of how to ingest a vendor dataset 202 to produce an ingesteddataset 204, which analysis operations 300 to perform on a vendordataset 202, and which match keys, match rules, and/or weights to use ona vendor dataset 202 responsive to ingestion/search metadata 122 andmatch metadata 146, including match key metadata 124, match rulemetadata 126, and/or ranking metadata 128, during runtime of the DAASsystem 110 as opposed to recompilation and redistribution of the DAASsystem software. Using one or more of ingestion/search metadata 122,match key metadata 124, match rule metadata 126, and ranking metadata128, the method 1500 may produce a more accurate query result 216 forcustomer systems 108 with minimum interruption to the DAAS system 110(e.g., without recompiling and redistributing the DAAS system 110software).

The operations in the flow diagram of FIG. 15 will be described withreference to the exemplary implementations of the other figures.However, it should be understood that the operations of the flow diagramcan be performed by implementations other than those discussed withreference to the other figures, and the implementations discussed withreference to these other figures can perform operations different thanthose discussed with reference to the flow diagrams. Although describedand shown in FIG. 15 in a particular order, the operations of the method1500 are not restricted to this order. For example, one or more of theoperations of the method 1500 may be performed in a different order orin partially or fully overlapping time periods. Accordingly, thedescription and depiction of the method 1500 is for illustrativepurposes and is not intended to restrict to a particular implementation.

In some implementations, one or more of the operations of the method1500 may be performed by components of the DAAS architecture 100. Forexample, one or more of the operations of the method 1500 may beperformed by one or more of the vendors systems 104, the customersystems 108, and/or the DAAS system 110. In particular, the ingestionservice 112, the analysis service 116, the index service 118, the searchservice 130, and/or the match service 140 may work in conjunction withthe vendor systems 104 and the customer systems 108 to perform theoperations of the method 1500.

In one implementation, the method 1500 may commence at operation 1501with receipt by the DAAS system 110 of a vendor dataset 202 representingdata of a vendor system 104 and a vendor to be made accessible to acustomer system 108 and to a customer via the customer system 108 of theDAAS system 110. In some implementations, the vendor dataset 202 may bereceived from a vendor system 104 via the ingestion interface 114. Thisreceipt of the vendor dataset 202 may be different in differentimplementations, such as a vendor system 104 pushing the vendor dataset202 to the DAAS system 110 and/or the DAAS system 110 accessing thevendor dataset 202 (e.g., via FTP, NFS, or a similar protocol/system).

At operation 1503, the DAAS system 110 may receive the ingestion/searchmetadata 122 that represents configuration information for a specificvendor dataset 202, vendor datasets 202 of a specific vendor system 104,or vendor datasets of a specific vendor. This receipt of theingestion/search metadata 122 may be different in differentimplementations, such as a vendor system 104 pushing theingestion/search metadata 122 to the DAAS system 110 and/or the DAASsystem 110 accessing the ingestion/search metadata 122 (e.g., via FTP,NFS, or a similar protocol/system). In some implementations, theingestion metadata 206 and the search metadata 214 may be within asingle data structure within the ingestion/search metadata 214. In theseimplementations, the ingestion metadata 206 and the search metadata 214may be received by the DAAS system 110 at the same time. When theingestion metadata 206 and the search metadata 214 are defined inseparate data structures, the ingestion metadata 206 and the searchmetadata 214 may be received by the DAAS system 110 at different times.In some implementations, the ingestion/search metadata 122 may bereceived during run time of the DAAS system 110 and may be used by theDAAS system 110 at run time to configure ingestion of the vendor dataset202 to produce an ingested dataset 204. In some implementations, theingestion/search metadata 122 may be received during run time of theDAAS system 110 and may be used by the DAAS system 110 at run time toconfigure analysis of the ingested dataset 204, and in particular thevendor dataset 202 within the ingested dataset 204, to produce ananalyzed dataset 210. In some implementations, the ingestion/searchmetadata 122, including one or more of the ingestion metadata 206 andthe search metadata 214, may be received by the DAAS system 110 at thesame time as the vendor dataset 202, before the vendor dataset 202, orafter the vendor dataset 202.

At operation 1505, the vendor dataset 202 may be ingested using theingestion/search metadata 122 (e.g., the ingestion metadata 206) togenerate an ingested dataset 204. In one implementation, the ingestionservice 112 may receive the vendor dataset 202 at operation 1501 and theingestion/search metadata 122 (e.g., the ingestion metadata 206) atoperation 1503. Based on these two inputs, the ingestion service 112 maythereafter ingest the received vendor dataset 202 at operation 1505 atrun time of the DAAS system 110, including the ingestion service 112,using the ingestion/search metadata 122 (e.g., the ingestion metadata206) to produce the ingested dataset 204. For example, theingestion/search metadata 122 (e.g., the ingestion metadata 206) may beused to configure the ingestion (e.g., the ingestion service 112) at runtime of the DAAS system 110 to produce the ingested dataset 204. Theingestion/search metadata 122, when used for ingesting, includes theingestion metadata 206 that maps one or more fields of the vendordataset 202 to fields defined within the DAAS system 110 and may be usedby the ingestion service 112 to verify that the structure of the vendordataset 202 (e.g., the set of fields in the vendor dataset 202) complieswith a structure agreed on between a vendor of the vendor system 104providing the vendor dataset 202 to the DAAS system 110 and anadministrator of the DAAS system 110. The ingested dataset 204 mayinclude the vendor dataset 202 and the ingestion metadata 206. Theingested dataset 204, including the vendor dataset 202 and the ingestionmetadata 206, may be later used by various other components of the DAASsystem 110 (e.g., the analysis service 116, the index service 118, thesearch service 130, and/or the match service 140) for accessing datafrom the vendor dataset 202.

In one implementation, an ingested dataset 204, and in particular thevendor dataset 202 within the ingested dataset 204, is analyzed by ananalysis service 116 using a set of analysis operations 300 that arereferenced in the ingestion/search metadata 122 (e.g., the searchmetadata 214) at operation 1507. These analysis operations 300 place theingested dataset 204 in a form that will produce a more accurate searchquery result 216A for the customer systems 108 in comparison to thevendor dataset 202 or the ingested dataset 204 on their own. In oneimplementation, the ingestion/search metadata 122 (e.g., the searchmetadata 214) may configure the analysis (e.g., the search service 116)at run time of the DAAS system 110. The ingestion/search metadata 122,when used for the analyzing, includes search metadata 214 that indicatesa set of analysis operations to be performed on the ingested dataset204.

At operation 1509, the DAAS system 110 may receive one or more of thematch key metadata 124, the match rule metadata 126, and the rankingmetadata 128 corresponding to or otherwise associated with the receivedvendor dataset 202, specific vendor system 104, or a specific vendor.This receipt of one or more of the match key metadata 124, the matchrule metadata 126, and the ranking metadata 128 may be different indifferent implementations, such as a vendor system 104 pushing the matchkey metadata 124, the match rule metadata 126, and/or the rankingmetadata 128 to the DAAS system 110 and/or the DAAS system 110 accessingthe match key metadata 124, the match rule metadata 126, and/or theranking metadata 128 (e.g., via FTP, NFS, or a similar protocol/system).Two or more of the match key metadata 124, the match rule metadata 126,and the ranking metadata 128 may be received by the DAAS system 110 atdifferent times. In some implementations, one or more of the match keymetadata 124, the match rule metadata 126, and/or the ranking metadata128 may be received during run time of the DAAS system 110 and may beused by the DAAS system 110 at run time to configure the DAAS system110, including the index service 118 and/or the match service 140. Inone implementation, one or more of the match key metadata 124, the matchrule metadata 126, and/or the ranking metadata 128 may be used at runtime of the DAAS system 110 for configuring match querying performed bythe DAAS system 110 on a dataset (e.g., the vendor dataset 202 via anindexed dataset 220). In some implementations, the match key metadata124, the match rule metadata 126, and/or the ranking metadata 128, maybe received by the DAAS system 110 at the same time as one or more ofthe vendor dataset 202, the ingestion metadata 206, and the searchmetadata 214, before one or more of the vendor dataset 202, theingestion metadata 206, and the search metadata 214, or after one ormore of the vendor dataset 202, the ingestion metadata 206, and thesearch metadata 214.

At operation 1511, the DAAS system 110 may either perform (1) a searchquery 142 on the vendor dataset 202 by using the analyzed dataset 210 toproduce a search query result 216A or (2) a match query 144 on thevendor dataset 202 using an indexed dataset 220 to produce a match queryresult 216A. When performing the match query 144, the DAAS system 110,and in particular the index service 118 and the match service 140, maybe configured at run time by one or more of the match key metadata 124,the match rule metadata 126, and the ranking metadata 128 for performingthe match query 144 to produce the match query result 216B. The matchkey metadata 124 may be used for configuring the index service 118 togenerate the indexed dataset 220 and the match service 140 (inparticular the candidate finder operation 1001), the match rule metadata126 may be used for configuring the match service 140 (in particular thematching operation 1003), and/or the ranking metadata 128 may be usedfor configuring the match service 140 (in particular the rankingoperation 1005) at run time of the DAAS system 110. At least one of thesearch query result 216A and/or the match query result 216A may beprovided to a corresponding customer system 108 and to a customer via acustomer system 108 at operation 1513.

As described above, the method 1500 utilizes the ingestion/searchmetadata 122 (e.g., the ingestion metadata 206 and/or the searchmetadata 214), the match key metadata 124, the match rule metadata 126,and/or the ranking metadata 128 to produce a more accurate query result216. Further, the ingestion/search metadata 122, the match key metadata124, the match rule metadata 126, and/or the ranking metadata 128 may beused during run time of the DAAS system 110 to configure the DAAS system110. Accordingly, operation of the DAAS system 110 may be configuredand/or reconfigured for a particular vendor system 104 and/or vendorwithout recompiling and redistributing the DAAS system 110 software. Byallowing the DAAS system 110 to remain running while configuring and/orreconfiguring (e.g., configuring and/or reconfiguring the ingestionservice 112, the analysis service 116, the index service 118, and/or thematch service 140), the method 1500 ensures minimal downtime for theDAAS system 110 while still producing accurate search query results 216Aand match query results 216B.

In one implementation, the DAAS system 110 may be realized using amicro-services architecture and/or using big-data technologies to storeand process vendor datasets 202 in a timely efficient manner. Thisenables the DAAS system 110 to host a multitude of data records (e.g.,thousand, millions, etc.) and serve these records (e.g., via searchquery results 216A and match query results 216B) efficiently to customersystems 108.

One or more parts of the above implementations may include softwareand/or a combination of software and hardware. An electronic devicestores and transmits (internally and/or with other electronic devicesover a network) code (which is composed of software instructions andwhich is sometimes referred to as computer program code or a computerprogram) and/or data using machine-readable media (also calledcomputer-readable media), such as machine-readable storage media (e.g.,magnetic disks, optical disks, read only memory (ROM), flash memory,phase change memory, and solid state drives (SSDs)) and machine-readabletransmission media (also called a carrier) (e.g., electrical, optical,radio, acoustical or another form of propagated signals—such as carrierwaves or infrared signals). Thus, an electronic device (e.g., acomputer) includes hardware and software, such as a set of one or moreprocessors coupled to one or more machine-readable storage media tostore code for execution on the set of processors and/or to store data.For instance, an electronic device may include non-volatile memory (withslower read/write times, e.g., magnetic disks, optical disks, read onlymemory (ROM), flash memory, phase change memory, and SSDs) and volatilememory (e.g., dynamic random access memory (DRAM), static random accessmemory (SRAM)), where the non-volatile memory persists the code/dataeven when the electronic device is turned off (when power is removed),and the electronic device copies that part of the code that is to beexecuted by the processor(s) of that electronic device from thenon-volatile memory into the volatile memory of that electronic deviceduring operation because volatile memory typically has faster read/writetimes. As another example, an electronic device may include anon-volatile memory (e.g., phase change memory) to store the code/datawhen the electronic device is turned off, and that same non-volatilememory has sufficiently fast read/write times such that, rather thancopying the part of the code/data to be executed into volatile memory,the code/data may be provided directly to the processor(s) (e.g., loadedinto a cache of the processor(s)); in other words, this non-volatilememory operates as both long-term storage and main memory, and thus theelectronic device may have no or only a small amount of DRAM for mainmemory. Typical electronic devices also include a set of one or morephysical network interface(s) to establish network connections (totransmit and/or receive code and/or data using propagating signals) withother electronic devices.

FIG. 16 illustrates an electronic device 1604 according to oneimplementation. FIG. 16 includes hardware 1640 comprising a set of oneor more processor(s) 1642, a set or one or more network interfaces 1644(wireless and/or wired), and non-transitory machine-readable storagemedia 1648 having stored therein software 1650. Each of the previouslydescribed vendor systems 104, customer systems 108, and the DAAS system110 may be implemented in one or more electronic devices 1604. In oneimplementation, each of the vendor systems 104 and customer systems 108is implemented in a separate one of the electronic devices 1604 (e.g.,in an end user electronic device operated by an end user; in which case,the software 1650 includes those elements necessary to interact with theDAAS system 110 directly or through an intermediate service layer (e.g.,an API, a web browser, a native client, a portal, a command-lineinterface, etc.)). Also, the DAAS system 110 is implemented in aseparate set of one or more of the electronic devices 1604 (e.g., inwhich case, the software 1650 is the DAAS system 110 software, includingone of the ingestion service 112, the analysis service 116, indexservice 118, and the match service 140). In operation, the end userelectronic devices and the electronic device(s) implementing the DAASsystem 110 would be commutatively coupled (e.g., by a network) and wouldestablish between them (or through one or more other layers (e.g., anintermediary device, which may be implemented in a set of one or moreelectronic devices that is separate from, overlapping with, or the sameas the set of one or more electronic devices on which the DAAS system110 is implemented (in which case, the software 1650 includes thesoftware to implement the intermediary devices))) connections forsubmitting vendor datasets 202 and metadata to the DAAS system 110 andreturning a search query result. Other configurations of electronicdevices may be used in other implementations (e.g., an implementation inwhich the end user client and the DAAS system 110 are implemented on asingle electronic device).

In electronic devices that use compute virtualization, the processor(s)1642 typically execute software to instantiate a virtualization layer1654 and software container(s) 1662A-R (e.g., with operatingsystem-level virtualization, the virtualization layer 1654 representsthe kernel of an operating system (or a shim executing on a baseoperating system) that allows for the creation of multiple softwarecontainers 1662A-R (representing separate user space instances and alsocalled virtualization engines, virtual private servers, or jails) thatmay each be used to execute a set of one or more applications. With fullvirtualization, the virtualization layer 1654 represents a hypervisor(sometimes referred to as a virtual machine monitor (VMM)) or ahypervisor executing on top of a host operating system, and the softwarecontainers 1662A-R each represent a tightly isolated form of softwarecontainer called a virtual machine that is run by the hypervisor and mayinclude a guest operating system. With para-virtualization, an operatingsystem or application running with a virtual machine may be aware of thepresence of virtualization for optimization purposes). Again, inelectronic devices where compute virtualization is used, duringoperation an instance of the software 1650 (illustrated as instance1676A) is executed within the software container 1662A on thevirtualization layer 1654. In electronic devices where computevirtualization is not used, the instance 1676A on top of a hostoperating system is executed on the “bare metal” electronic device 1604.The instantiation of the instance 1676A, as well as the virtualizationlayer 1654 and software containers 1662A-R if implemented, arecollectively referred to as software instance(s) 1652.

Alternative implementations of an electronic device may have numerousvariations from that described above. For example, customized hardwareand/or accelerators may also be used in an electronic device.

A network device (ND) is an electronic device that communicativelyinterconnects other electronic devices on the network (e.g., othernetwork devices, end-user devices). Some network devices are “multipleservices network devices” that provide support for multiple networkingfunctions (e.g., routing, bridging, switching, Layer 2 aggregation,session border control, Quality of Service, and/or subscribermanagement), and/or provide support for multiple application services(e.g., data, voice, and video).

FIG. 17 shows a block diagram of an environment 1700 where an on-demand,metadata driven DAAS may be implemented. An on-demand service is madeavailable to outside users that do not need to necessarily be concernedwith building and/or maintaining a system, but instead may be availablefor their use when the users need the service (e.g., on the demand ofthe users). A system 1701 includes hardware and software, and comprisesthe DAAS system 110. Further, in one implementation, the system 1701 isa multi-tenant cloud computing architecture supporting multipleservices, such as software as a service (e.g., DAAS, customerrelationship management (CRM)), platform as a service (e.g., executionruntime, database, application development tools; such as Force.com®,Heroku™, and Database.com™ by salesforce.com, Inc.), and/orinfrastructure as a service (virtual machines, servers, storage). In animplementation, system 1701 may include an application platform 1703that enables platform as a service for creating, managing and executingone or more applications developed by the provider of the DAAS system110, users accessing the system 1701 via user systems 1705 (e.g., vendorsystems 104 and customer systems 108), or third-party applicationdevelopers accessing the system 1701 via user systems 1705.

Network 1707 is any network or combination of networks of devices thatcommunicate with one another. For example, network 1707 can be any oneor any combination of a LAN (local area network), WAN (wide areanetwork), telephone network, wireless network, point-to-point network,star network, token ring network, hub network, or other appropriateconfiguration. As the most common type of computer network in currentuse is a TCP/IP (Transfer Control Protocol and Internet Protocol)network, such as the global internetwork of networks often referred toas the “Internet” with a capital “I,” that network will be used in someof the examples herein. However, it should be understood that thenetworks that the one or more implementations may use are not solimited, although TCP/IP is a frequently implemented protocol.

Each user system 1705 is an end user electronic device, such as adesktop personal computer, workstation, laptop, Personal DigitalAssistant (PDA), cell phone, etc. Each user system 1705 also typicallyincludes one or more user interface devices, such as a keyboard, amouse, a trackball, a touch pad, a touch screen, a pen or the like, forinteracting with a graphical user interface (GUI) provided on a display(e.g., a monitor screen, a LCD display, etc.) in conjunction with pages,forms, applications and other information provided by system 1701 orother systems or servers. For example, the user interface device can beused to access data and applications hosted by system 1701, and toperform searches on stored data, and otherwise allow a user to interactwith various GUI pages that may be presented to a user. User systems1705 might communicate with system 1701 using TCP/IP and, at a highernetwork level, use other Internet protocols to communicate, such asHypertext Transfer Protocol (HTTP), FTP, Andrew File System (AFS),Wireless Application Protocol (WAP), etc. In an example where HTTP isused, user system 1705 might include an HTTP client commonly referred toas a “browser” for sending and receiving HTTP messages to and from aserver at system 1701 allowing a user of user system 1705 to access,process and view information, pages and applications available to itfrom system 1701 over network 1707. Such a server might be implementedas the sole network interface between system 1701 and network 1707, butother techniques might be used as well or instead. In someimplementations, the interface between system 1701 and network 1707includes load sharing functionality, such as round-robin HTTP requestdistributors to balance loads and distribute incoming HTTP requestsevenly over a plurality of servers. However, other alternativeconfigurations may be used instead.

One arrangement for elements of system 1701 is shown in FIG. 17,including network interface 1709, application platform 1703,multi-tenant database 1711 for tenant data 1713, system data storage1715 for system data 1717 accessible to system 1701 and possiblymultiple tenants, program code 1719 (a runtime engine that materializesapplication data from metadata; that is, there is a clear separation ofthe compiled runtime engine (also known as the system kernel), tenantdata, and the metadata that describes each application, which make itpossible to independently update the system kernel and tenant-specificapplications and schemas, with virtually no risk of one affecting theothers), and DAAS system 110 for implementing various functions ofsystem 1701.

In one implementation, multi-tenant database 1711 stores user/tenantdata and application metadata. For example, a copy of a user's mostrecently used (MRU) items might be stored. Similarly, a copy of MRUitems for an entire organization that is a tenant might be stored. Inone implementation, the user/tenant data may include the vendor dataset202, the ingested dataset 204, the analyzed dataset 210, and the indexeddataset 220, while the application metadata may include the ingestionmetadata 206, the search metadata 214, and the match metadata 146,including the match key metadata 124, the match rule metadata 126, andthe ranking metadata 128. The tenant data may be stored in variousdatabases, such as one or more Oracle™ databases.

In one implementation, application platform 1703 includes an applicationsetup mechanism that supports application developers' creation andmanagement of applications, which may be saved as metadata by saveroutines. Invocations to such applications, including the DAAS system110, may be coded using Procedural Language/Structured Object QueryLanguage (PL/SOQL) that provides a programming language style interface.A detailed description of some PL/SOQL language implementations isdiscussed in U.S. Pat. No. 7,730,478 entitled, METHOD AND SYSTEM FORALLOWING ACCESS TO DEVELOPED APPLICATIONS VIA A MULTI-TENANT ON-DEMANDDATABASE SERVICE, by Craig Weissman, filed Sep. 21, 2007. Invocations toapplications may be detected by one or more system processes, whichmanages retrieving application metadata for the subscriber making theinvocation and executing the metadata as an application in a virtualmachine.

In certain implementations, one or more servers of system 1701 isconfigured to handle requests for any user associated with anyorganization that is a tenant. Because it is desirable to be able to addand remove servers from the server pool at any time and for any reason,there is preferably no server affinity for a user and/or organization toa specific server. In one implementation, therefore, an interface systemimplementing a load balancing function (e.g., an F5 Big-IP loadbalancer) is communicably coupled between the servers of system 1701 andthe user systems 1705 to distribute requests to the servers. In oneimplementation, the load balancer uses a least connections algorithm toroute user requests to the servers. Other examples of load balancingalgorithms, such as round robin and observed response time, also can beused. For example, in certain implementations, three consecutiverequests from the same user could hit three different servers, and threerequests from different users could hit the same server. In this manner,system 1701 is multi-tenant, wherein system 1701 handles storage of, andaccess to, different database objects, data and applications acrossdisparate users and organizations.

In certain implementations, user systems 1705 (which may be clientsystems) communicate with the servers of system 1701 to request andupdate system-level and tenant-level data from system 1701 that mayrequire sending one or more queries to multi-tenant database 1711 and/orsystem data storage 1715. System 1701 (e.g., a server in system 1701)automatically generates one or more Structured Query Language (SQL)statements (e.g., one or more SQL queries) that are designed to accessthe desired information.

In some multi-tenant database systems, tenants may be allowed to createand store custom objects, or they may be allowed to customize standardentities or objects, for example by creating custom fields for standardobjects, including custom index fields. U.S. Pat. No. 7,779,039, filedApr. 2, 2004, entitled “Custom Entities and Fields in a Multi-TenantDatabase System,” describes systems and methods for creating customdatabase objects as well as customizing standard database objects in amulti-tenant DBMS. In certain implementations, for example, all datarecords of a custom database object are stored in a single multi-tenantphysical table, which may contain multiple logical database objects perorganization. It is transparent to customers that their multipledatabase objects are in fact stored in one large table or that theirdata may be stored in the same table as the data of other customers.

In the above description, numerous specific details such as resourcepartitioning/sharing/duplication implementations, types andinterrelationships of system components, and logicpartitioning/integration choices are set forth in order to provide amore thorough understanding. It will be appreciated, however, by oneskilled in the art, that the implementations described herein may bepracticed without such specific details. In other instances, controlstructures, logic implementations, opcodes, means to specify operands,and full software instruction sequences have not been shown in detailsince those of ordinary skill in the art, with the includeddescriptions, will be able to implement what is described without undueexperimentation.

References in the specification to “one implementation,” “animplementation,” “an example implementation,” etc., indicate that theimplementation described may include a particular feature, structure, orcharacteristic, but every implementation may not necessarily include theparticular feature, structure, or characteristic. Moreover, such phrasesare not necessarily referring to the same implementation. Further, whena particular feature, structure, or characteristic is described inconnection with an implementation, it is submitted that it is within theknowledge of one skilled in the art to affect such feature, structure,or characteristic in connection with other implementations whether ornot explicitly described.

Bracketed text and blocks with dashed borders (e.g., large dashes, smalldashes, dot-dash, and dots) may be used herein to illustrate optionaloperations and/or structures that add additional features to someimplementations. However, such notation should not be taken to mean thatthese are the only options or optional operations, and/or that blockswith solid borders are not optional in certain implementations.

In the following description and claims, the term “coupled,” along withits derivatives, may be used. “Coupled” is used to indicate that two ormore elements, which may or may not be in direct physical or electricalcontact with each other, co-operate or interact with each other.

The operations in the flow diagrams are be described with reference tothe exemplary implementations in the other figures. However, theoperations of the flow diagrams can be performed by implementationsother than those discussed with reference to the other figures, and theimplementations discussed with reference to these other figures canperform operations different than those discussed with reference to theflow diagrams.

While the flow diagrams in the figures show a particular order ofoperations performed by certain implementations, it should be understoodthat such order is exemplary (e.g., alternative implementations mayperform the operations in a different order, combine certain operations,overlap certain operations, etc.).

While implementations have been described in relation to a metadataexpressed in XML, other data structures or languages may be utilized.Therefore, the implementations are not limited to XML. In addition,while implementations have been described in relation to ingestion andanalysis being separate operations, alternative implementations could beimplemented such that ingestion and analysis are a singleservice/operation. Such implementations could utilize a singlepiece/file of metadata.

While the above description includes several exemplary implementations,those skilled in the art will recognize that the invention is notlimited to the implementations described and can be practiced withmodification and alteration within the spirit and scope of the appendedclaims. The description is thus illustrative instead of limiting.

What is claimed is:
 1. A method for configuring the operation of thesoftware of a data as a service (DAAS) system during run time as opposedto recompiling and redistributing the software of the DAAS system,wherein the configuring includes at least one of configuring ingestionof a vendor dataset to produce an ingested dataset and which analysisoperations to perform on the vendor dataset to produce an analyzeddataset, and wherein the configuring also includes at least one of howto search the vendor dataset based on a search query from a customer toallow the customer to locate a new record from the vendor dataset andhow to match records in the vendor dataset with a match query from thecustomer to provide an updated record to the customer, comprising:receiving, by the DAAS system, the vendor dataset representing data of avendor to be made accessible to a customer of the DAAS system;receiving, by the DAAS system, ingestion/search metadata that representsconfiguration information for the vendor; receiving, by the DAAS system,one or more of match key metadata, match rule metadata, and rankingmetadata that represents configuration information for the vendor;ingesting, by the DAAS system, the vendor dataset to produce theingested dataset; analyzing, by the DAAS system, the vendor dataset toproduce the analyzed dataset, wherein the ingestion/search metadataconfigures at least one of the ingesting and the analyzing at run timeof the DAAS system, wherein the ingestion/search metadata when used forthe ingesting includes ingestion metadata that describes a structure ofthe vendor dataset, and wherein the ingestion/search metadata when usedfor the analyzing includes search metadata that indicates a set ofanalysis operations to be performed on the vendor dataset; querying, bythe DAAS system, the vendor dataset to produce a query result, whereinwhen the querying is performed based on the search query, the analyzeddataset is used to query the vendor dataset to determine a search queryresult and wherein when the querying is performed based on the matchquery at least one of the match key metadata, the match rule metadata,and the ranking metadata configures the querying at run time of the DAASsystem, wherein the match key metadata indicates fields in the vendordataset to generate one or more match keys for the vendor dataset andthe match query to determine one or more candidate records from thevendor dataset, the match rule metadata indicates one or more matchrules to apply to the one or more candidate records to determine one ormore matched records, and the ranking metadata indicates weights toapply to the matched records to determine a match query result; andproviding at least one of the search query result and the match queryresult to the customer of the DAAS system.
 2. The method of claim 1,wherein the set of analysis operations are indicated in the searchmetadata through one or more analysis types, wherein each analysis typein the one more analysis types identifies an ordered set of one or moreanalysis operations from the set of analysis operations.
 3. The methodof claim 2, wherein the one or more analysis types are defined in a dataanalysis dictionary, wherein the data analysis dictionary is a statemachine in which each analysis operation in the set of analysisoperations is represented by a state in the state machine and theordered group of analysis operations for each analysis type is definedby a pathway through states in the state machine.
 4. The method of claim1, wherein the set of analysis operations include one or more of anormalization operation, a tokenization operation, a character filteringoperation, a token filtering operation, a data field generationoperation, an indexing operation, and a storing operation.
 5. The methodof claim 1, wherein the ingestion/search metadata and at least one ofthe match key metadata, the match rule metadata, and the rankingmetadata is received at run time of the DAAS system.
 6. The method ofclaim 1, wherein querying the vendor dataset based on the match querycomprises: generating the one or more match keys for each record in thevendor dataset to produce an indexed dataset; generating the one or morematch keys for the match query; and comparing the one or more match keysfor each record in the indexed dataset with the one or more match keysgenerated for the query to determine the one or more candidate recordsfrom the indexed dataset.
 7. The method of claim 6, wherein querying thevendor dataset based on the match query further comprises: applying theone or more match rules to the one or more candidate records todetermine one or more matched records, wherein each of the one or morematch rules include one or more of a method and a threshold; applyingthe weights to the one or more matched records to determine a ranked setof records; and comparing records in the ranked set of records torecords in the vendor dataset to determine matching records in thevendor dataset, wherein the matching records in the vendor dataset arethe match query result.
 8. A data as a service (DAAS) system, includinga processor coupled to a memory, that configures operation of thesoftware of the DAAS system during run time as opposed to recompilingand redistributing the software of the DAAS system, wherein theconfiguring includes at least one of configuring ingestion of a vendordataset to produce an ingested dataset and which analysis operations toperform on the vendor dataset to produce an analyzed dataset and whereinthe configuring also includes at least one of how to search the vendordataset based on a search query from a customer to allow the customer tolocate a new record from the vendor dataset and how to match records inthe vendor dataset with a match query from the customer to provide anupdated record to the customer, comprising: an ingestion service toingest the vendor dataset to produce the ingested dataset, wherein thevendor dataset represents data of a vendor to be made accessible to acustomer of the DAAS system; an analysis service to analyze the vendordataset to produce the analyzed dataset, wherein ingestion/searchmetadata configures at least one of the ingestion service and theanalysis service at run time of the DAAS system, wherein theingestion/search metadata when used for the ingestion service includesingestion metadata that describes a structure of the vendor dataset, andwherein the ingestion/search metadata when used for the analysis serviceincludes search metadata that indicates a set of analysis operations tobe performed on the vendor dataset; a search service to query the vendordataset to produce a search query result using the search query and theanalyzed dataset; a match service to query the vendor dataset to producea match query result using at least one of match key metadata, matchrule metadata, and ranking metadata to configure match service at runtime of the DAAS system, wherein the match key metadata indicates fieldsin the vendor dataset to generate one or more match keys for the vendordataset and the match query to determine one or more candidate recordsfrom the vendor dataset, the match rule metadata indicates one or morematch rules to apply to the one or more candidate records to determineone or more matched records, and the ranking metadata indicates weightsto apply to the matched records to determine the match query result; anda serving interface to provide at least one of the search query resultand the match query result to the customer of the DAAS system.
 9. TheDAAS system of claim 8, wherein the set of analysis operations areindicated in the search metadata through one or more analysis types,wherein each analysis type in the one more analysis types identifies anordered set of one or more analysis operations from the set of analysisoperations.
 10. The DAAS system of claim 9, wherein the one or moreanalysis types are defined in a data analysis dictionary, wherein thedata analysis dictionary is a state machine in which each analysisoperation in the set of analysis operations is represented by a state inthe state machine and the ordered group of analysis operations for eachanalysis type is defined by a pathway through states in the statemachine.
 11. The DAAS system of claim 8, wherein the set of analysisoperations include one or more of a normalization operation, atokenization operation, a character filtering operation, a tokenfiltering operation, a data field generation operation, an indexingoperation, and a storing operation.
 12. The DAAS system of claim 1,wherein the ingestion/search metadata and at least one of the match keymetadata, the match rule metadata, and the ranking metadata is receivedat run time of the DAAS system.
 13. A non-transitory computer-readablestorage medium storing instructions which, when executed by a set of oneor more processors of an electronic device, cause the electronic deviceto: receive a vendor dataset representing data of a vendor to be madeaccessible to a customer of the electronic device; receiveingestion/search metadata that represents configuration information forthe vendor; receive one or more of match key metadata, match rulemetadata, and ranking metadata that represents configuration informationfor the vendor; ingest the vendor dataset to produce an ingesteddataset; analyze the vendor dataset to produce an analyzed dataset,wherein the ingestion/search metadata configures at least one of theingesting and the analyzing at run time of the electronic device,wherein the ingestion/search metadata when used for the ingestingincludes ingestion metadata that describes a structure of the vendordataset, and wherein the ingestion/search metadata when used for theanalyzing includes search metadata that indicates a set of analysisoperations to be performed on the vendor dataset; query the vendordataset to produce a query result, wherein when the querying isperformed based on a search query, the analyzed dataset is used to querythe vendor dataset to determine a search query result and wherein whenthe querying is performed based on a match query at least one of thematch key metadata, the match rule metadata, and the ranking metadataconfigures the querying at run time of the electronic device, whereinthe match key metadata indicates fields in the vendor dataset togenerate one or more match keys for the vendor dataset and the matchquery to determine one or more candidate records from the vendordataset, the match rule metadata indicates one or more match rules toapply to the one or more candidate records to determine one or morematched records, and the ranking metadata indicates weights to apply tothe matched records to determine a match query result; and provide atleast one of the search query result and the match query result to thecustomer of the electronic device.
 14. The non-transitorycomputer-readable storage medium of claim 13, wherein the set ofanalysis operations are indicated in the search metadata through one ormore analysis types, wherein each analysis type in the one more analysistypes identifies an ordered set of one or more analysis operations fromthe set of analysis operations.
 15. The non-transitory computer-readablestorage medium of claim 14, wherein the one or more analysis types aredefined in a data analysis dictionary, wherein the data analysisdictionary is a state machine in which each analysis operation in theset of analysis operations is represented by a state in the statemachine and the ordered group of analysis operations for each analysistype is defined by a pathway through states in the state machine. 16.The non-transitory computer-readable storage medium of claim 13, whereinthe set of analysis operations include one or more of a normalizationoperation, a tokenization operation, a character filtering operation, atoken filtering operation, a data field generation operation, anindexing operation, and a storing operation.
 17. The non-transitorycomputer-readable storage medium of claim 13, wherein theingestion/search metadata and at least one of the match key metadata,the match rule metadata, and the ranking metadata is received at runtime of the electronic device.
 18. The non-transitory computer-readablestorage medium of claim 13, wherein querying the vendor dataset based onthe match query comprises: generating the one or more match keys foreach record in the vendor dataset to produce an indexed dataset;generating the one or more match keys for the match query; and comparingthe one or more match keys for each record in the indexed dataset withthe one or more match keys generated for the query to determine the oneor more candidate records from the indexed dataset.
 19. Thenon-transitory computer-readable storage medium of claim 18, whereinquerying the vendor dataset based on the match query further comprises:applying the one or more match rules to the one or more candidaterecords to determine one or more matched records, wherein each of theone or more match rules include one or more of a method and a threshold;applying the weights to the one or more matched records to determine aranked set of records; and comparing records in the ranked set ofrecords to records in the vendor dataset to determine matching recordsin the vendor dataset, wherein the matching records in the vendordataset are the match query result.